Overview

Dataset statistics

Number of variables24
Number of observations9134
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.7 MiB
Average record size in memory192.0 B

Variable types

Text1
Categorical13
Numeric8
Boolean1
DateTime1

Alerts

customer has unique valuesUnique
income has 2317 (25.4%) zerosZeros
months_since_last_claim has 314 (3.4%) zerosZeros
number_of_open_complaints has 7252 (79.4%) zerosZeros

Reproduction

Analysis started2024-01-25 16:19:18.317062
Analysis finished2024-01-25 16:19:28.020493
Duration9.7 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

customer
Text

UNIQUE 

Distinct9134
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size71.5 KiB
2024-01-25T13:19:28.190038image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters63938
Distinct characters37
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9134 ?
Unique (%)100.0%

Sample

1st rowBU79786
2nd rowQZ44356
3rd rowAI49188
4th rowWW63253
5th rowHB64268
ValueCountFrequency (%)
bu79786 1
 
< 0.1%
oc83172 1
 
< 0.1%
oe15005 1
 
< 0.1%
rz33670 1
 
< 0.1%
dy87989 1
 
< 0.1%
ai49188 1
 
< 0.1%
ww63253 1
 
< 0.1%
hb64268 1
 
< 0.1%
xz87318 1
 
< 0.1%
ao98601 1
 
< 0.1%
Other values (9124) 9124
99.9%
2024-01-25T13:19:28.560435image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7 4750
 
7.4%
5 4729
 
7.4%
3 4718
 
7.4%
9 4694
 
7.3%
8 4686
 
7.3%
6 4686
 
7.3%
4 4646
 
7.3%
1 4612
 
7.2%
2 4473
 
7.0%
0 3677
 
5.8%
Other values (27) 18267
28.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45671
71.4%
Uppercase Letter 18251
 
28.5%
Lowercase Letter 16
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
W 746
 
4.1%
Z 722
 
4.0%
M 722
 
4.0%
F 721
 
4.0%
E 720
 
3.9%
C 718
 
3.9%
P 718
 
3.9%
N 716
 
3.9%
L 714
 
3.9%
X 713
 
3.9%
Other values (16) 11041
60.5%
Decimal Number
ValueCountFrequency (%)
7 4750
10.4%
5 4729
10.4%
3 4718
10.3%
9 4694
10.3%
8 4686
10.3%
6 4686
10.3%
4 4646
10.2%
1 4612
10.1%
2 4473
9.8%
0 3677
8.1%
Lowercase Letter
ValueCountFrequency (%)
o 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45671
71.4%
Latin 18267
 
28.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
W 746
 
4.1%
Z 722
 
4.0%
M 722
 
4.0%
F 721
 
3.9%
E 720
 
3.9%
C 718
 
3.9%
P 718
 
3.9%
N 716
 
3.9%
L 714
 
3.9%
X 713
 
3.9%
Other values (17) 11057
60.5%
Common
ValueCountFrequency (%)
7 4750
10.4%
5 4729
10.4%
3 4718
10.3%
9 4694
10.3%
8 4686
10.3%
6 4686
10.3%
4 4646
10.2%
1 4612
10.1%
2 4473
9.8%
0 3677
8.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 63938
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7 4750
 
7.4%
5 4729
 
7.4%
3 4718
 
7.4%
9 4694
 
7.3%
8 4686
 
7.3%
6 4686
 
7.3%
4 4646
 
7.3%
1 4612
 
7.2%
2 4473
 
7.0%
0 3677
 
5.8%
Other values (27) 18267
28.6%

state
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size71.5 KiB
California
3150 
Oregon
2601 
Arizona
1703 
Nevada
882 
Washington
798 

Length

Max length10
Median length7
Mean length7.9153711
Min length6

Characters and Unicode

Total characters72299
Distinct characters20
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWashington
2nd rowArizona
3rd rowNevada
4th rowCalifornia
5th rowWashington

Common Values

ValueCountFrequency (%)
California 3150
34.5%
Oregon 2601
28.5%
Arizona 1703
18.6%
Nevada 882
 
9.7%
Washington 798
 
8.7%

Length

2024-01-25T13:19:28.741261image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-25T13:19:28.875317image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
california 3150
34.5%
oregon 2601
28.5%
arizona 1703
18.6%
nevada 882
 
9.7%
washington 798
 
8.7%

Most occurring characters

ValueCountFrequency (%)
a 10565
14.6%
n 9050
12.5%
i 8801
12.2%
o 8252
11.4%
r 7454
10.3%
e 3483
 
4.8%
g 3399
 
4.7%
C 3150
 
4.4%
f 3150
 
4.4%
l 3150
 
4.4%
Other values (10) 11845
16.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 63165
87.4%
Uppercase Letter 9134
 
12.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 10565
16.7%
n 9050
14.3%
i 8801
13.9%
o 8252
13.1%
r 7454
11.8%
e 3483
 
5.5%
g 3399
 
5.4%
f 3150
 
5.0%
l 3150
 
5.0%
z 1703
 
2.7%
Other values (5) 4158
 
6.6%
Uppercase Letter
ValueCountFrequency (%)
C 3150
34.5%
O 2601
28.5%
A 1703
18.6%
N 882
 
9.7%
W 798
 
8.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 72299
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 10565
14.6%
n 9050
12.5%
i 8801
12.2%
o 8252
11.4%
r 7454
10.3%
e 3483
 
4.8%
g 3399
 
4.7%
C 3150
 
4.4%
f 3150
 
4.4%
l 3150
 
4.4%
Other values (10) 11845
16.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 72299
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 10565
14.6%
n 9050
12.5%
i 8801
12.2%
o 8252
11.4%
r 7454
10.3%
e 3483
 
4.8%
g 3399
 
4.7%
C 3150
 
4.4%
f 3150
 
4.4%
l 3150
 
4.4%
Other values (10) 11845
16.4%

customer_lifetime_value
Real number (ℝ)

Distinct8041
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8004.9405
Minimum1898.0077
Maximum83325.381
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size71.5 KiB
2024-01-25T13:19:29.035235image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1898.0077
5-th percentile2475.109
Q13994.2518
median5780.1822
Q38962.167
95-th percentile22064.361
Maximum83325.381
Range81427.374
Interquartile range (IQR)4967.9152

Descriptive statistics

Standard deviation6870.9676
Coefficient of variation (CV)0.85834087
Kurtosis13.823533
Mean8004.9405
Median Absolute Deviation (MAD)2467.8936
Skewness3.0322803
Sum73117126
Variance47210196
MonotonicityNot monotonic
2024-01-25T13:19:29.209496image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7200.664877 6
 
0.1%
17497.52201 6
 
0.1%
2248.449633 6
 
0.1%
8092.87696 6
 
0.1%
6057.07208 6
 
0.1%
4250.282624 6
 
0.1%
8838.085637 6
 
0.1%
22103.5072 6
 
0.1%
26197.41498 6
 
0.1%
2574.020376 6
 
0.1%
Other values (8031) 9074
99.3%
ValueCountFrequency (%)
1898.007675 1
 
< 0.1%
1898.683686 1
 
< 0.1%
1904.000852 1
 
< 0.1%
1918.1197 1
 
< 0.1%
1940.981221 1
 
< 0.1%
1994.774936 1
 
< 0.1%
2004.350666 6
0.1%
2009.772923 1
 
< 0.1%
2030.783687 1
 
< 0.1%
2034.993043 1
 
< 0.1%
ValueCountFrequency (%)
83325.38119 1
< 0.1%
74228.51604 1
< 0.1%
73225.95652 1
< 0.1%
67907.2705 1
< 0.1%
66025.75407 1
< 0.1%
64618.75715 1
< 0.1%
61850.18803 1
< 0.1%
61134.68307 1
< 0.1%
60556.19213 1
< 0.1%
58753.88046 1
< 0.1%

response
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
False
7826 
True
1308 
ValueCountFrequency (%)
False 7826
85.7%
True 1308
 
14.3%
2024-01-25T13:19:29.341219image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

coverage
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size71.5 KiB
Basic
5568 
Extended
2742 
Premium
824 

Length

Max length8
Median length5
Mean length6.081016
Min length5

Characters and Unicode

Total characters55544
Distinct characters15
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBasic
2nd rowExtended
3rd rowPremium
4th rowBasic
5th rowBasic

Common Values

ValueCountFrequency (%)
Basic 5568
61.0%
Extended 2742
30.0%
Premium 824
 
9.0%

Length

2024-01-25T13:19:29.460968image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-25T13:19:29.568812image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
basic 5568
61.0%
extended 2742
30.0%
premium 824
 
9.0%

Most occurring characters

ValueCountFrequency (%)
i 6392
11.5%
e 6308
11.4%
B 5568
10.0%
a 5568
10.0%
s 5568
10.0%
c 5568
10.0%
d 5484
9.9%
E 2742
 
4.9%
x 2742
 
4.9%
t 2742
 
4.9%
Other values (5) 6862
12.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 46410
83.6%
Uppercase Letter 9134
 
16.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 6392
13.8%
e 6308
13.6%
a 5568
12.0%
s 5568
12.0%
c 5568
12.0%
d 5484
11.8%
x 2742
5.9%
t 2742
5.9%
n 2742
5.9%
m 1648
 
3.6%
Other values (2) 1648
 
3.6%
Uppercase Letter
ValueCountFrequency (%)
B 5568
61.0%
E 2742
30.0%
P 824
 
9.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 55544
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 6392
11.5%
e 6308
11.4%
B 5568
10.0%
a 5568
10.0%
s 5568
10.0%
c 5568
10.0%
d 5484
9.9%
E 2742
 
4.9%
x 2742
 
4.9%
t 2742
 
4.9%
Other values (5) 6862
12.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 55544
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 6392
11.5%
e 6308
11.4%
B 5568
10.0%
a 5568
10.0%
s 5568
10.0%
c 5568
10.0%
d 5484
9.9%
E 2742
 
4.9%
x 2742
 
4.9%
t 2742
 
4.9%
Other values (5) 6862
12.4%

education
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size71.5 KiB
Bachelor
2748 
College
2681 
High School or Below
2622 
Master
741 
Doctor
342 

Length

Max length20
Median length8
Mean length10.914057
Min length6

Characters and Unicode

Total characters99689
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBachelor
2nd rowBachelor
3rd rowBachelor
4th rowBachelor
5th rowBachelor

Common Values

ValueCountFrequency (%)
Bachelor 2748
30.1%
College 2681
29.4%
High School or Below 2622
28.7%
Master 741
 
8.1%
Doctor 342
 
3.7%

Length

2024-01-25T13:19:29.702384image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-25T13:19:29.840710image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
bachelor 2748
16.2%
college 2681
15.8%
high 2622
15.4%
school 2622
15.4%
or 2622
15.4%
below 2622
15.4%
master 741
 
4.4%
doctor 342
 
2.0%

Most occurring characters

ValueCountFrequency (%)
o 16601
16.7%
l 13354
13.4%
e 11473
11.5%
h 7992
8.0%
7866
7.9%
r 6453
 
6.5%
c 5712
 
5.7%
B 5370
 
5.4%
g 5303
 
5.3%
a 3489
 
3.5%
Other values (9) 16076
16.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 77445
77.7%
Uppercase Letter 14378
 
14.4%
Space Separator 7866
 
7.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 16601
21.4%
l 13354
17.2%
e 11473
14.8%
h 7992
10.3%
r 6453
 
8.3%
c 5712
 
7.4%
g 5303
 
6.8%
a 3489
 
4.5%
i 2622
 
3.4%
w 2622
 
3.4%
Other values (2) 1824
 
2.4%
Uppercase Letter
ValueCountFrequency (%)
B 5370
37.3%
C 2681
18.6%
H 2622
18.2%
S 2622
18.2%
M 741
 
5.2%
D 342
 
2.4%
Space Separator
ValueCountFrequency (%)
7866
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 91823
92.1%
Common 7866
 
7.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 16601
18.1%
l 13354
14.5%
e 11473
12.5%
h 7992
8.7%
r 6453
 
7.0%
c 5712
 
6.2%
B 5370
 
5.8%
g 5303
 
5.8%
a 3489
 
3.8%
C 2681
 
2.9%
Other values (8) 13395
14.6%
Common
ValueCountFrequency (%)
7866
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 99689
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 16601
16.7%
l 13354
13.4%
e 11473
11.5%
h 7992
8.0%
7866
7.9%
r 6453
 
6.5%
c 5712
 
5.7%
B 5370
 
5.4%
g 5303
 
5.3%
a 3489
 
3.5%
Other values (9) 16076
16.1%
Distinct59
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size71.5 KiB
Minimum2011-01-01 00:00:00
Maximum2011-02-28 00:00:00
2024-01-25T13:19:29.990976image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-25T13:19:30.159815image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

employmentstatus
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size71.5 KiB
Employed
5698 
Unemployed
2317 
Medical Leave
 
432
Disabled
 
405
Retired
 
282

Length

Max length13
Median length8
Mean length8.7129407
Min length7

Characters and Unicode

Total characters79584
Distinct characters23
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEmployed
2nd rowUnemployed
3rd rowEmployed
4th rowUnemployed
5th rowEmployed

Common Values

ValueCountFrequency (%)
Employed 5698
62.4%
Unemployed 2317
25.4%
Medical Leave 432
 
4.7%
Disabled 405
 
4.4%
Retired 282
 
3.1%

Length

2024-01-25T13:19:30.315978image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-25T13:19:30.434341image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
employed 5698
59.6%
unemployed 2317
24.2%
medical 432
 
4.5%
leave 432
 
4.5%
disabled 405
 
4.2%
retired 282
 
2.9%

Most occurring characters

ValueCountFrequency (%)
e 12597
15.8%
d 9134
11.5%
l 8852
11.1%
m 8015
10.1%
p 8015
10.1%
o 8015
10.1%
y 8015
10.1%
E 5698
7.2%
U 2317
 
2.9%
n 2317
 
2.9%
Other values (13) 6609
8.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 69586
87.4%
Uppercase Letter 9566
 
12.0%
Space Separator 432
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 12597
18.1%
d 9134
13.1%
l 8852
12.7%
m 8015
11.5%
p 8015
11.5%
o 8015
11.5%
y 8015
11.5%
n 2317
 
3.3%
a 1269
 
1.8%
i 1119
 
1.6%
Other values (6) 2238
 
3.2%
Uppercase Letter
ValueCountFrequency (%)
E 5698
59.6%
U 2317
24.2%
M 432
 
4.5%
L 432
 
4.5%
D 405
 
4.2%
R 282
 
2.9%
Space Separator
ValueCountFrequency (%)
432
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 79152
99.5%
Common 432
 
0.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 12597
15.9%
d 9134
11.5%
l 8852
11.2%
m 8015
10.1%
p 8015
10.1%
o 8015
10.1%
y 8015
10.1%
E 5698
7.2%
U 2317
 
2.9%
n 2317
 
2.9%
Other values (12) 6177
7.8%
Common
ValueCountFrequency (%)
432
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 79584
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 12597
15.8%
d 9134
11.5%
l 8852
11.1%
m 8015
10.1%
p 8015
10.1%
o 8015
10.1%
y 8015
10.1%
E 5698
7.2%
U 2317
 
2.9%
n 2317
 
2.9%
Other values (13) 6609
8.3%

gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size71.5 KiB
F
4658 
M
4476 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9134
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowF
3rd rowF
4th rowM
5th rowM

Common Values

ValueCountFrequency (%)
F 4658
51.0%
M 4476
49.0%

Length

2024-01-25T13:19:30.782388image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-25T13:19:30.888905image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
f 4658
51.0%
m 4476
49.0%

Most occurring characters

ValueCountFrequency (%)
F 4658
51.0%
M 4476
49.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 9134
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F 4658
51.0%
M 4476
49.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 9134
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 4658
51.0%
M 4476
49.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9134
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F 4658
51.0%
M 4476
49.0%

income
Real number (ℝ)

ZEROS 

Distinct5694
Distinct (%)62.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37657.38
Minimum0
Maximum99981
Zeros2317
Zeros (%)25.4%
Negative0
Negative (%)0.0%
Memory size71.5 KiB
2024-01-25T13:19:31.027961image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median33889.5
Q362320
95-th percentile90374.35
Maximum99981
Range99981
Interquartile range (IQR)62320

Descriptive statistics

Standard deviation30379.905
Coefficient of variation (CV)0.80674505
Kurtosis-1.094326
Mean37657.38
Median Absolute Deviation (MAD)28681
Skewness0.28688728
Sum3.4396251 × 108
Variance9.2293861 × 108
MonotonicityNot monotonic
2024-01-25T13:19:31.194450image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2317
 
25.4%
95697 12
 
0.1%
27972 7
 
0.1%
25370 7
 
0.1%
61108 7
 
0.1%
40864 7
 
0.1%
25965 7
 
0.1%
20978 7
 
0.1%
26876 7
 
0.1%
33190 7
 
0.1%
Other values (5684) 6749
73.9%
ValueCountFrequency (%)
0 2317
25.4%
10037 1
 
< 0.1%
10074 1
 
< 0.1%
10097 1
 
< 0.1%
10105 1
 
< 0.1%
10147 1
 
< 0.1%
10180 1
 
< 0.1%
10194 1
 
< 0.1%
10211 1
 
< 0.1%
10237 1
 
< 0.1%
ValueCountFrequency (%)
99981 1
 
< 0.1%
99961 1
 
< 0.1%
99960 1
 
< 0.1%
99934 1
 
< 0.1%
99875 1
 
< 0.1%
99874 1
 
< 0.1%
99845 6
0.1%
99841 1
 
< 0.1%
99839 1
 
< 0.1%
99824 1
 
< 0.1%

location_code
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size71.5 KiB
Suburban
5779 
Rural
1773 
Urban
1582 

Length

Max length8
Median length8
Mean length6.8980731
Min length5

Characters and Unicode

Total characters63007
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSuburban
2nd rowSuburban
3rd rowSuburban
4th rowSuburban
5th rowRural

Common Values

ValueCountFrequency (%)
Suburban 5779
63.3%
Rural 1773
 
19.4%
Urban 1582
 
17.3%

Length

2024-01-25T13:19:31.344087image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-25T13:19:31.457353image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
suburban 5779
63.3%
rural 1773
 
19.4%
urban 1582
 
17.3%

Most occurring characters

ValueCountFrequency (%)
u 13331
21.2%
b 13140
20.9%
r 9134
14.5%
a 9134
14.5%
n 7361
11.7%
S 5779
9.2%
R 1773
 
2.8%
l 1773
 
2.8%
U 1582
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 53873
85.5%
Uppercase Letter 9134
 
14.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u 13331
24.7%
b 13140
24.4%
r 9134
17.0%
a 9134
17.0%
n 7361
13.7%
l 1773
 
3.3%
Uppercase Letter
ValueCountFrequency (%)
S 5779
63.3%
R 1773
 
19.4%
U 1582
 
17.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 63007
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
u 13331
21.2%
b 13140
20.9%
r 9134
14.5%
a 9134
14.5%
n 7361
11.7%
S 5779
9.2%
R 1773
 
2.8%
l 1773
 
2.8%
U 1582
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 63007
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
u 13331
21.2%
b 13140
20.9%
r 9134
14.5%
a 9134
14.5%
n 7361
11.7%
S 5779
9.2%
R 1773
 
2.8%
l 1773
 
2.8%
U 1582
 
2.5%

marital_status
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size71.5 KiB
Married
5298 
Single
2467 
Divorced
1369 

Length

Max length8
Median length7
Mean length6.8797898
Min length6

Characters and Unicode

Total characters62840
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMarried
2nd rowSingle
3rd rowMarried
4th rowMarried
5th rowSingle

Common Values

ValueCountFrequency (%)
Married 5298
58.0%
Single 2467
27.0%
Divorced 1369
 
15.0%

Length

2024-01-25T13:19:31.587297image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-25T13:19:31.709222image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
married 5298
58.0%
single 2467
27.0%
divorced 1369
 
15.0%

Most occurring characters

ValueCountFrequency (%)
r 11965
19.0%
i 9134
14.5%
e 9134
14.5%
d 6667
10.6%
M 5298
8.4%
a 5298
8.4%
S 2467
 
3.9%
n 2467
 
3.9%
g 2467
 
3.9%
l 2467
 
3.9%
Other values (4) 5476
8.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 53706
85.5%
Uppercase Letter 9134
 
14.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 11965
22.3%
i 9134
17.0%
e 9134
17.0%
d 6667
12.4%
a 5298
9.9%
n 2467
 
4.6%
g 2467
 
4.6%
l 2467
 
4.6%
v 1369
 
2.5%
o 1369
 
2.5%
Uppercase Letter
ValueCountFrequency (%)
M 5298
58.0%
S 2467
27.0%
D 1369
 
15.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 62840
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 11965
19.0%
i 9134
14.5%
e 9134
14.5%
d 6667
10.6%
M 5298
8.4%
a 5298
8.4%
S 2467
 
3.9%
n 2467
 
3.9%
g 2467
 
3.9%
l 2467
 
3.9%
Other values (4) 5476
8.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 62840
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 11965
19.0%
i 9134
14.5%
e 9134
14.5%
d 6667
10.6%
M 5298
8.4%
a 5298
8.4%
S 2467
 
3.9%
n 2467
 
3.9%
g 2467
 
3.9%
l 2467
 
3.9%
Other values (4) 5476
8.7%

monthly_premium_auto
Real number (ℝ)

Distinct202
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.219291
Minimum61
Maximum298
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size71.5 KiB
2024-01-25T13:19:31.847130image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum61
5-th percentile62
Q168
median83
Q3109
95-th percentile163.35
Maximum298
Range237
Interquartile range (IQR)41

Descriptive statistics

Standard deviation34.407967
Coefficient of variation (CV)0.3691078
Kurtosis6.193605
Mean93.219291
Median Absolute Deviation (MAD)18
Skewness2.1235464
Sum851465
Variance1183.9082
MonotonicityNot monotonic
2024-01-25T13:19:32.013831image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65 348
 
3.8%
66 307
 
3.4%
71 304
 
3.3%
73 302
 
3.3%
63 289
 
3.2%
69 287
 
3.1%
67 284
 
3.1%
61 277
 
3.0%
72 275
 
3.0%
68 270
 
3.0%
Other values (192) 6191
67.8%
ValueCountFrequency (%)
61 277
3.0%
62 257
2.8%
63 289
3.2%
64 266
2.9%
65 348
3.8%
66 307
3.4%
67 284
3.1%
68 270
3.0%
69 287
3.1%
70 249
2.7%
ValueCountFrequency (%)
298 1
 
< 0.1%
297 2
 
< 0.1%
296 1
 
< 0.1%
295 2
 
< 0.1%
290 1
 
< 0.1%
287 1
 
< 0.1%
286 1
 
< 0.1%
285 2
 
< 0.1%
284 1
 
< 0.1%
283 7
0.1%

months_since_last_claim
Real number (ℝ)

ZEROS 

Distinct36
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.097
Minimum0
Maximum35
Zeros314
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size71.5 KiB
2024-01-25T13:19:32.169913image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median14
Q323
95-th percentile33
Maximum35
Range35
Interquartile range (IQR)17

Descriptive statistics

Standard deviation10.073257
Coefficient of variation (CV)0.66723564
Kurtosis-1.0736677
Mean15.097
Median Absolute Deviation (MAD)8
Skewness0.27858631
Sum137896
Variance101.4705
MonotonicityNot monotonic
2024-01-25T13:19:32.315337image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
3 381
 
4.2%
6 364
 
4.0%
1 345
 
3.8%
4 335
 
3.7%
7 333
 
3.6%
2 329
 
3.6%
0 314
 
3.4%
5 313
 
3.4%
10 306
 
3.4%
11 297
 
3.3%
Other values (26) 5817
63.7%
ValueCountFrequency (%)
0 314
3.4%
1 345
3.8%
2 329
3.6%
3 381
4.2%
4 335
3.7%
5 313
3.4%
6 364
4.0%
7 333
3.6%
8 279
3.1%
9 250
2.7%
ValueCountFrequency (%)
35 142
1.6%
34 169
1.9%
33 179
2.0%
32 138
1.5%
31 190
2.1%
30 182
2.0%
29 206
2.3%
28 201
2.2%
27 182
2.0%
26 186
2.0%
Distinct100
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.064594
Minimum0
Maximum99
Zeros83
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size71.5 KiB
2024-01-25T13:19:32.479075image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q124
median48
Q371
95-th percentile93
Maximum99
Range99
Interquartile range (IQR)47

Descriptive statistics

Standard deviation27.905991
Coefficient of variation (CV)0.5805935
Kurtosis-1.1330459
Mean48.064594
Median Absolute Deviation (MAD)24
Skewness0.040164962
Sum439022
Variance778.74432
MonotonicityNot monotonic
2024-01-25T13:19:32.642004image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59 142
 
1.6%
61 128
 
1.4%
50 125
 
1.4%
44 115
 
1.3%
3 114
 
1.2%
10 114
 
1.2%
56 112
 
1.2%
38 110
 
1.2%
34 109
 
1.2%
35 109
 
1.2%
Other values (90) 7956
87.1%
ValueCountFrequency (%)
0 83
0.9%
1 85
0.9%
2 89
1.0%
3 114
1.2%
4 91
1.0%
5 87
1.0%
6 73
0.8%
7 79
0.9%
8 81
0.9%
9 96
1.1%
ValueCountFrequency (%)
99 78
0.9%
98 54
0.6%
97 52
0.6%
96 67
0.7%
95 77
0.8%
94 72
0.8%
93 90
1.0%
92 74
0.8%
91 75
0.8%
90 84
0.9%

number_of_open_complaints
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.384388
Minimum0
Maximum5
Zeros7252
Zeros (%)79.4%
Negative0
Negative (%)0.0%
Memory size71.5 KiB
2024-01-25T13:19:32.774211image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.91038353
Coefficient of variation (CV)2.3683974
Kurtosis7.7493085
Mean0.384388
Median Absolute Deviation (MAD)0
Skewness2.7832631
Sum3511
Variance0.82879817
MonotonicityNot monotonic
2024-01-25T13:19:32.906135image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 7252
79.4%
1 1011
 
11.1%
2 374
 
4.1%
3 292
 
3.2%
4 149
 
1.6%
5 56
 
0.6%
ValueCountFrequency (%)
0 7252
79.4%
1 1011
 
11.1%
2 374
 
4.1%
3 292
 
3.2%
4 149
 
1.6%
5 56
 
0.6%
ValueCountFrequency (%)
5 56
 
0.6%
4 149
 
1.6%
3 292
 
3.2%
2 374
 
4.1%
1 1011
 
11.1%
0 7252
79.4%

number_of_policies
Real number (ℝ)

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9661704
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size71.5 KiB
2024-01-25T13:19:33.021894image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q34
95-th percentile8
Maximum9
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.3901818
Coefficient of variation (CV)0.80581407
Kurtosis0.36315659
Mean2.9661704
Median Absolute Deviation (MAD)1
Skewness1.2533327
Sum27093
Variance5.7129691
MonotonicityNot monotonic
2024-01-25T13:19:33.155440image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 3251
35.6%
2 2294
25.1%
3 1168
 
12.8%
7 433
 
4.7%
9 416
 
4.6%
4 409
 
4.5%
5 407
 
4.5%
8 384
 
4.2%
6 372
 
4.1%
ValueCountFrequency (%)
1 3251
35.6%
2 2294
25.1%
3 1168
 
12.8%
4 409
 
4.5%
5 407
 
4.5%
6 372
 
4.1%
7 433
 
4.7%
8 384
 
4.2%
9 416
 
4.6%
ValueCountFrequency (%)
9 416
 
4.6%
8 384
 
4.2%
7 433
 
4.7%
6 372
 
4.1%
5 407
 
4.5%
4 409
 
4.5%
3 1168
 
12.8%
2 2294
25.1%
1 3251
35.6%

policy_type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size71.5 KiB
Personal Auto
6788 
Corporate Auto
1968 
Special Auto
 
378

Length

Max length14
Median length13
Mean length13.174075
Min length12

Characters and Unicode

Total characters120332
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCorporate Auto
2nd rowPersonal Auto
3rd rowPersonal Auto
4th rowCorporate Auto
5th rowPersonal Auto

Common Values

ValueCountFrequency (%)
Personal Auto 6788
74.3%
Corporate Auto 1968
 
21.5%
Special Auto 378
 
4.1%

Length

2024-01-25T13:19:33.310749image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-25T13:19:33.426024image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
auto 9134
50.0%
personal 6788
37.2%
corporate 1968
 
10.8%
special 378
 
2.1%

Most occurring characters

ValueCountFrequency (%)
o 19858
16.5%
t 11102
9.2%
r 10724
8.9%
9134
7.6%
A 9134
7.6%
u 9134
7.6%
a 9134
7.6%
e 9134
7.6%
l 7166
 
6.0%
P 6788
 
5.6%
Other values (7) 19024
15.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 92930
77.2%
Uppercase Letter 18268
 
15.2%
Space Separator 9134
 
7.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 19858
21.4%
t 11102
11.9%
r 10724
11.5%
u 9134
9.8%
a 9134
9.8%
e 9134
9.8%
l 7166
 
7.7%
n 6788
 
7.3%
s 6788
 
7.3%
p 2346
 
2.5%
Other values (2) 756
 
0.8%
Uppercase Letter
ValueCountFrequency (%)
A 9134
50.0%
P 6788
37.2%
C 1968
 
10.8%
S 378
 
2.1%
Space Separator
ValueCountFrequency (%)
9134
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 111198
92.4%
Common 9134
 
7.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 19858
17.9%
t 11102
10.0%
r 10724
9.6%
A 9134
8.2%
u 9134
8.2%
a 9134
8.2%
e 9134
8.2%
l 7166
 
6.4%
P 6788
 
6.1%
n 6788
 
6.1%
Other values (6) 12236
11.0%
Common
ValueCountFrequency (%)
9134
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 120332
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 19858
16.5%
t 11102
9.2%
r 10724
8.9%
9134
7.6%
A 9134
7.6%
u 9134
7.6%
a 9134
7.6%
e 9134
7.6%
l 7166
 
6.0%
P 6788
 
5.6%
Other values (7) 19024
15.8%

policy
Categorical

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size71.5 KiB
Personal L3
3426 
Personal L2
2122 
Personal L1
1240 
Corporate L3
1014 
Corporate L2
595 
Other values (4)
737 

Length

Max length12
Median length11
Mean length11.174075
Min length10

Characters and Unicode

Total characters102064
Distinct characters19
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCorporate L3
2nd rowPersonal L3
3rd rowPersonal L3
4th rowCorporate L2
5th rowPersonal L1

Common Values

ValueCountFrequency (%)
Personal L3 3426
37.5%
Personal L2 2122
23.2%
Personal L1 1240
 
13.6%
Corporate L3 1014
 
11.1%
Corporate L2 595
 
6.5%
Corporate L1 359
 
3.9%
Special L2 164
 
1.8%
Special L3 148
 
1.6%
Special L1 66
 
0.7%

Length

2024-01-25T13:19:33.560548image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-25T13:19:33.699811image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
personal 6788
37.2%
l3 4588
25.1%
l2 2881
15.8%
corporate 1968
 
10.8%
l1 1665
 
9.1%
special 378
 
2.1%

Most occurring characters

ValueCountFrequency (%)
r 10724
10.5%
o 10724
10.5%
L 9134
8.9%
e 9134
8.9%
9134
8.9%
a 9134
8.9%
l 7166
7.0%
P 6788
6.7%
n 6788
6.7%
s 6788
6.7%
Other values (9) 16550
16.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 65528
64.2%
Uppercase Letter 18268
 
17.9%
Space Separator 9134
 
8.9%
Decimal Number 9134
 
8.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 10724
16.4%
o 10724
16.4%
e 9134
13.9%
a 9134
13.9%
l 7166
10.9%
n 6788
10.4%
s 6788
10.4%
p 2346
 
3.6%
t 1968
 
3.0%
c 378
 
0.6%
Uppercase Letter
ValueCountFrequency (%)
L 9134
50.0%
P 6788
37.2%
C 1968
 
10.8%
S 378
 
2.1%
Decimal Number
ValueCountFrequency (%)
3 4588
50.2%
2 2881
31.5%
1 1665
 
18.2%
Space Separator
ValueCountFrequency (%)
9134
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 83796
82.1%
Common 18268
 
17.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 10724
12.8%
o 10724
12.8%
L 9134
10.9%
e 9134
10.9%
a 9134
10.9%
l 7166
8.6%
P 6788
8.1%
n 6788
8.1%
s 6788
8.1%
p 2346
 
2.8%
Other values (5) 5070
6.1%
Common
ValueCountFrequency (%)
9134
50.0%
3 4588
25.1%
2 2881
 
15.8%
1 1665
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 102064
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 10724
10.5%
o 10724
10.5%
L 9134
8.9%
e 9134
8.9%
9134
8.9%
a 9134
8.9%
l 7166
7.0%
P 6788
6.7%
n 6788
6.7%
s 6788
6.7%
Other values (9) 16550
16.2%

renew_offer_type
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size71.5 KiB
Offer1
3752 
Offer2
2926 
Offer3
1432 
Offer4
1024 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters54804
Distinct characters8
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOffer1
2nd rowOffer3
3rd rowOffer1
4th rowOffer1
5th rowOffer1

Common Values

ValueCountFrequency (%)
Offer1 3752
41.1%
Offer2 2926
32.0%
Offer3 1432
 
15.7%
Offer4 1024
 
11.2%

Length

2024-01-25T13:19:33.846792image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-25T13:19:33.952819image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
offer1 3752
41.1%
offer2 2926
32.0%
offer3 1432
 
15.7%
offer4 1024
 
11.2%

Most occurring characters

ValueCountFrequency (%)
f 18268
33.3%
O 9134
16.7%
e 9134
16.7%
r 9134
16.7%
1 3752
 
6.8%
2 2926
 
5.3%
3 1432
 
2.6%
4 1024
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 36536
66.7%
Uppercase Letter 9134
 
16.7%
Decimal Number 9134
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3752
41.1%
2 2926
32.0%
3 1432
 
15.7%
4 1024
 
11.2%
Lowercase Letter
ValueCountFrequency (%)
f 18268
50.0%
e 9134
25.0%
r 9134
25.0%
Uppercase Letter
ValueCountFrequency (%)
O 9134
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 45670
83.3%
Common 9134
 
16.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
f 18268
40.0%
O 9134
20.0%
e 9134
20.0%
r 9134
20.0%
Common
ValueCountFrequency (%)
1 3752
41.1%
2 2926
32.0%
3 1432
 
15.7%
4 1024
 
11.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 54804
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
f 18268
33.3%
O 9134
16.7%
e 9134
16.7%
r 9134
16.7%
1 3752
 
6.8%
2 2926
 
5.3%
3 1432
 
2.6%
4 1024
 
1.9%

sales_channel
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size71.5 KiB
Agent
3477 
Branch
2567 
Call Center
1765 
Web
1325 

Length

Max length11
Median length6
Mean length6.1503175
Min length3

Characters and Unicode

Total characters56177
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAgent
2nd rowAgent
3rd rowAgent
4th rowCall Center
5th rowAgent

Common Values

ValueCountFrequency (%)
Agent 3477
38.1%
Branch 2567
28.1%
Call Center 1765
19.3%
Web 1325
 
14.5%

Length

2024-01-25T13:19:34.096079image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-25T13:19:34.226033image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
agent 3477
31.9%
branch 2567
23.6%
call 1765
16.2%
center 1765
16.2%
web 1325
 
12.2%

Most occurring characters

ValueCountFrequency (%)
e 8332
14.8%
n 7809
13.9%
t 5242
9.3%
r 4332
7.7%
a 4332
7.7%
C 3530
 
6.3%
l 3530
 
6.3%
A 3477
 
6.2%
g 3477
 
6.2%
B 2567
 
4.6%
Other values (5) 9549
17.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 43513
77.5%
Uppercase Letter 10899
 
19.4%
Space Separator 1765
 
3.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 8332
19.1%
n 7809
17.9%
t 5242
12.0%
r 4332
10.0%
a 4332
10.0%
l 3530
8.1%
g 3477
8.0%
c 2567
 
5.9%
h 2567
 
5.9%
b 1325
 
3.0%
Uppercase Letter
ValueCountFrequency (%)
C 3530
32.4%
A 3477
31.9%
B 2567
23.6%
W 1325
 
12.2%
Space Separator
ValueCountFrequency (%)
1765
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 54412
96.9%
Common 1765
 
3.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 8332
15.3%
n 7809
14.4%
t 5242
9.6%
r 4332
8.0%
a 4332
8.0%
C 3530
6.5%
l 3530
6.5%
A 3477
6.4%
g 3477
6.4%
B 2567
 
4.7%
Other values (4) 7784
14.3%
Common
ValueCountFrequency (%)
1765
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 56177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 8332
14.8%
n 7809
13.9%
t 5242
9.3%
r 4332
7.7%
a 4332
7.7%
C 3530
 
6.3%
l 3530
 
6.3%
A 3477
 
6.2%
g 3477
 
6.2%
B 2567
 
4.6%
Other values (5) 9549
17.0%

total_claim_amount
Real number (ℝ)

Distinct5106
Distinct (%)55.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean434.08879
Minimum0.099007
Maximum2893.2397
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size71.5 KiB
2024-01-25T13:19:34.371681image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.099007
5-th percentile52.261227
Q1272.25824
median383.94543
Q3547.51484
95-th percentile960.1154
Maximum2893.2397
Range2893.1407
Interquartile range (IQR)275.25659

Descriptive statistics

Standard deviation290.50009
Coefficient of variation (CV)0.66921813
Kurtosis5.979401
Mean434.08879
Median Absolute Deviation (MAD)144.05457
Skewness1.7149658
Sum3964967
Variance84390.303
MonotonicityNot monotonic
2024-01-25T13:19:34.525154image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
316.8 116
 
1.3%
292.8 110
 
1.2%
312 108
 
1.2%
350.4 105
 
1.1%
331.2 102
 
1.1%
321.6 100
 
1.1%
302.4 94
 
1.0%
345.6 90
 
1.0%
355.2 85
 
0.9%
326.4 82
 
0.9%
Other values (5096) 8142
89.1%
ValueCountFrequency (%)
0.099007 1
< 0.1%
0.382107 1
< 0.1%
0.42331 1
< 0.1%
0.517753 1
< 0.1%
0.769185 1
< 0.1%
0.887629 1
< 0.1%
1.208908 1
< 0.1%
1.332349 1
< 0.1%
1.48947 1
< 0.1%
1.587888 1
< 0.1%
ValueCountFrequency (%)
2893.239678 1
< 0.1%
2759.794354 1
< 0.1%
2552.343856 1
< 0.1%
2452.894264 1
< 0.1%
2345.413441 1
< 0.1%
2327.166394 1
< 0.1%
2306.508397 1
< 0.1%
2294.631639 1
< 0.1%
2275.265075 1
< 0.1%
2270.508697 1
< 0.1%

vehicle_class
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size71.5 KiB
Four-Door Car
4621 
Two-Door Car
1886 
SUV
1796 
Sports Car
484 
Luxury SUV
 
184

Length

Max length13
Median length13
Mean length10.554303
Min length3

Characters and Unicode

Total characters96403
Distinct characters20
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTwo-Door Car
2nd rowFour-Door Car
3rd rowTwo-Door Car
4th rowSUV
5th rowFour-Door Car

Common Values

ValueCountFrequency (%)
Four-Door Car 4621
50.6%
Two-Door Car 1886
20.6%
SUV 1796
 
19.7%
Sports Car 484
 
5.3%
Luxury SUV 184
 
2.0%
Luxury Car 163
 
1.8%

Length

2024-01-25T13:19:34.677994image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-25T13:19:34.800333image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
car 7154
43.4%
four-door 4621
28.1%
suv 1980
 
12.0%
two-door 1886
 
11.4%
sports 484
 
2.9%
luxury 347
 
2.1%

Most occurring characters

ValueCountFrequency (%)
o 20005
20.8%
r 19113
19.8%
7338
 
7.6%
C 7154
 
7.4%
a 7154
 
7.4%
- 6507
 
6.7%
D 6507
 
6.7%
u 5315
 
5.5%
F 4621
 
4.8%
S 2464
 
2.6%
Other values (10) 10225
10.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 55619
57.7%
Uppercase Letter 26939
27.9%
Space Separator 7338
 
7.6%
Dash Punctuation 6507
 
6.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 20005
36.0%
r 19113
34.4%
a 7154
 
12.9%
u 5315
 
9.6%
w 1886
 
3.4%
p 484
 
0.9%
t 484
 
0.9%
s 484
 
0.9%
x 347
 
0.6%
y 347
 
0.6%
Uppercase Letter
ValueCountFrequency (%)
C 7154
26.6%
D 6507
24.2%
F 4621
17.2%
S 2464
 
9.1%
U 1980
 
7.3%
V 1980
 
7.3%
T 1886
 
7.0%
L 347
 
1.3%
Space Separator
ValueCountFrequency (%)
7338
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6507
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 82558
85.6%
Common 13845
 
14.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 20005
24.2%
r 19113
23.2%
C 7154
 
8.7%
a 7154
 
8.7%
D 6507
 
7.9%
u 5315
 
6.4%
F 4621
 
5.6%
S 2464
 
3.0%
U 1980
 
2.4%
V 1980
 
2.4%
Other values (8) 6265
 
7.6%
Common
ValueCountFrequency (%)
7338
53.0%
- 6507
47.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 96403
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 20005
20.8%
r 19113
19.8%
7338
 
7.6%
C 7154
 
7.4%
a 7154
 
7.4%
- 6507
 
6.7%
D 6507
 
6.7%
u 5315
 
5.5%
F 4621
 
4.8%
S 2464
 
2.6%
Other values (10) 10225
10.6%

vehicle_size
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size71.5 KiB
Medsize
6424 
Small
1764 
Large
946 

Length

Max length7
Median length7
Mean length6.4066127
Min length5

Characters and Unicode

Total characters58518
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMedsize
2nd rowMedsize
3rd rowMedsize
4th rowMedsize
5th rowMedsize

Common Values

ValueCountFrequency (%)
Medsize 6424
70.3%
Small 1764
 
19.3%
Large 946
 
10.4%

Length

2024-01-25T13:19:34.956796image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-25T13:19:35.088039image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
medsize 6424
70.3%
small 1764
 
19.3%
large 946
 
10.4%

Most occurring characters

ValueCountFrequency (%)
e 13794
23.6%
M 6424
11.0%
d 6424
11.0%
s 6424
11.0%
i 6424
11.0%
z 6424
11.0%
l 3528
 
6.0%
a 2710
 
4.6%
S 1764
 
3.0%
m 1764
 
3.0%
Other values (3) 2838
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 49384
84.4%
Uppercase Letter 9134
 
15.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 13794
27.9%
d 6424
13.0%
s 6424
13.0%
i 6424
13.0%
z 6424
13.0%
l 3528
 
7.1%
a 2710
 
5.5%
m 1764
 
3.6%
r 946
 
1.9%
g 946
 
1.9%
Uppercase Letter
ValueCountFrequency (%)
M 6424
70.3%
S 1764
 
19.3%
L 946
 
10.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 58518
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 13794
23.6%
M 6424
11.0%
d 6424
11.0%
s 6424
11.0%
i 6424
11.0%
z 6424
11.0%
l 3528
 
6.0%
a 2710
 
4.6%
S 1764
 
3.0%
m 1764
 
3.0%
Other values (3) 2838
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 58518
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 13794
23.6%
M 6424
11.0%
d 6424
11.0%
s 6424
11.0%
i 6424
11.0%
z 6424
11.0%
l 3528
 
6.0%
a 2710
 
4.6%
S 1764
 
3.0%
m 1764
 
3.0%
Other values (3) 2838
 
4.8%

Interactions

2024-01-25T13:19:26.389064image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-25T13:19:19.506220image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-25T13:19:20.526685image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-25T13:19:21.504450image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-25T13:19:22.425882image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-25T13:19:23.414865image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-25T13:19:24.508841image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-25T13:19:25.443729image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-25T13:19:26.513741image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-25T13:19:19.653692image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-25T13:19:20.660546image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-25T13:19:21.622720image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-25T13:19:22.553037image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-25T13:19:23.539885image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-25T13:19:24.639862image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-25T13:19:25.570815image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-25T13:19:26.626761image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-25T13:19:19.788123image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-25T13:19:20.802075image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-25T13:19:21.731818image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-25T13:19:22.674486image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-25T13:19:23.652143image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-25T13:19:24.759726image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-25T13:19:25.688847image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-25T13:19:26.753042image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-25T13:19:19.913857image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-25T13:19:20.935285image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-25T13:19:21.838558image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-25T13:19:22.791914image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-25T13:19:23.769111image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-25T13:19:24.872293image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-25T13:19:25.802665image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-25T13:19:26.902727image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-25T13:19:20.043965image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-25T13:19:21.050708image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-25T13:19:21.957303image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-25T13:19:22.916110image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-25T13:19:23.884972image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-25T13:19:24.993971image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-25T13:19:25.922251image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-25T13:19:27.035028image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-25T13:19:20.158308image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-25T13:19:21.159757image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-25T13:19:22.067867image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-25T13:19:23.033748image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-25T13:19:23.998239image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-25T13:19:25.106474image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-25T13:19:26.038087image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-25T13:19:27.148562image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-25T13:19:20.276375image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-25T13:19:21.271366image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-25T13:19:22.180846image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-25T13:19:23.147083image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-25T13:19:24.271057image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-25T13:19:25.212452image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-25T13:19:26.148783image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-25T13:19:27.268250image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-25T13:19:20.401397image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-25T13:19:21.389129image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-25T13:19:22.318374image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-25T13:19:23.281950image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-25T13:19:24.394854image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-25T13:19:25.330715image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-25T13:19:26.271007image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Missing values

2024-01-25T13:19:27.460893image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-25T13:19:27.842043image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

customerstatecustomer_lifetime_valueresponsecoverageeducationeffective_to_dateemploymentstatusgenderincomelocation_codemarital_statusmonthly_premium_automonths_since_last_claimmonths_since_policy_inceptionnumber_of_open_complaintsnumber_of_policiespolicy_typepolicyrenew_offer_typesales_channeltotal_claim_amountvehicle_classvehicle_size
0BU79786Washington2763.519279NoBasicBachelor2/24/11EmployedF56274SuburbanMarried6932501Corporate AutoCorporate L3Offer1Agent384.811147Two-Door CarMedsize
1QZ44356Arizona6979.535903NoExtendedBachelor1/31/11UnemployedF0SuburbanSingle94134208Personal AutoPersonal L3Offer3Agent1131.464935Four-Door CarMedsize
2AI49188Nevada12887.431650NoPremiumBachelor2/19/11EmployedF48767SuburbanMarried108183802Personal AutoPersonal L3Offer1Agent566.472247Two-Door CarMedsize
3WW63253California7645.861827NoBasicBachelor1/20/11UnemployedM0SuburbanMarried106186507Corporate AutoCorporate L2Offer1Call Center529.881344SUVMedsize
4HB64268Washington2813.692575NoBasicBachelor2/3/11EmployedM43836RuralSingle73124401Personal AutoPersonal L1Offer1Agent138.130879Four-Door CarMedsize
5OC83172Oregon8256.297800YesBasicBachelor1/25/11EmployedF62902RuralMarried69149402Personal AutoPersonal L3Offer2Web159.383042Two-Door CarMedsize
6XZ87318Oregon5380.898636YesBasicCollege2/24/11EmployedF55350SuburbanMarried6701309Corporate AutoCorporate L3Offer1Agent321.600000Four-Door CarMedsize
7CF85061Arizona7216.100311NoPremiumMaster1/18/11UnemployedM0UrbanSingle10106804Corporate AutoCorporate L3Offer1Agent363.029680Four-Door CarMedsize
8DY87989Oregon24127.504020YesBasicBachelor1/26/11Medical LeaveM14072SuburbanDivorced7113302Corporate AutoCorporate L3Offer1Agent511.200000Four-Door CarMedsize
9BQ94931Oregon7388.178085NoExtendedCollege2/17/11EmployedF28812UrbanMarried9317708Special AutoSpecial L2Offer2Branch425.527834Four-Door CarMedsize
customerstatecustomer_lifetime_valueresponsecoverageeducationeffective_to_dateemploymentstatusgenderincomelocation_codemarital_statusmonthly_premium_automonths_since_last_claimmonths_since_policy_inceptionnumber_of_open_complaintsnumber_of_policiespolicy_typepolicyrenew_offer_typesales_channeltotal_claim_amountvehicle_classvehicle_size
9124CB59349California16261.585500NoExtendedMaster1/20/11EmployedM60646SuburbanMarried134314202Personal AutoPersonal L3Offer2Agent643.200000SUVMedsize
9125RX91025California19872.262000NoPremiumHigh School or Below1/31/11UnemployedM0SuburbanSingle185263502Personal AutoPersonal L3Offer1Agent1950.725547SUVSmall
9126AC13887California4628.995325NoBasicBachelor1/9/11UnemployedM0SuburbanSingle67252104Corporate AutoCorporate L1Offer1Branch482.400000Two-Door CarMedsize
9127TF56202California5032.165498NoBasicCollege2/12/11EmployedM66367SuburbanDivorced6464803Personal AutoPersonal L3Offer2Call Center307.200000Two-Door CarSmall
9128YM19146California4100.398533NoPremiumCollege1/6/11EmployedF47761SuburbanSingle104165801Personal AutoPersonal L2Offer1Branch541.282007Four-Door CarLarge
9129LA72316California23405.987980NoBasicBachelor2/10/11EmployedM71941UrbanMarried73188902Personal AutoPersonal L1Offer2Web198.234764Four-Door CarMedsize
9130PK87824California3096.511217YesExtendedCollege2/12/11EmployedF21604SuburbanDivorced79142801Corporate AutoCorporate L3Offer1Branch379.200000Four-Door CarMedsize
9131TD14365California8163.890428NoExtendedBachelor2/6/11UnemployedM0SuburbanSingle8593732Corporate AutoCorporate L2Offer1Branch790.784983Four-Door CarMedsize
9132UP19263California7524.442436NoExtendedCollege2/3/11EmployedM21941SuburbanMarried9634303Personal AutoPersonal L2Offer3Branch691.200000Four-Door CarLarge
9133Y167826California2611.836866NoExtendedCollege2/14/11UnemployedM0SuburbanSingle7739001Corporate AutoCorporate L3Offer4Call Center369.600000Two-Door CarMedsize